Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features

Abstract

The electrocardiogram (ECG) is the most commonly used tool for diagnosing cardiovascular diseases. Recently, there have been a number of attempts to classify cardiac arrhythmias using machine learning and deep learning techniques. In this study, we propose a novel method to generate the gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) from one-dimensional signals. From the GLCM and GLRLM, we extracted morphological features for automatic ECG signal classification. The extracted features were combined with six machine learning algorithms (decision tree, k-nearest neighbor, naïve Bayes, logistic regression, random forest, and XGBoost) to classify cardiac arrhythmias. Experiments were conducted on a 12-lead ECG database collected from Chapman University and Shaoxing People’s Hospital. Of the six machine learning algorithms, combining XGBoost with the proposed features yielded an accuracy of 90.46%, an AUC of 0.982, a sensitivity of 0.892, a precision of 0.900, and an F1 score of 0.895 and presented better results than wavelet features with XGBoost. The experimental results show the effectiveness of the proposed feature extraction algorithm.

Cardiac Magnetic Resonance Left Ventricle Segmentation and Function Evaluation Using a Trained Deep-Learning Model

“…Cine MRI has been routinely performed for evaluation of LV structure and function in cardiovascular MR exams. Deep learning and machine leaning have demonstrated promise in several aspects of the cardiac research and clinical workflow, including but not limited to prediction of cardiac left ventricular kinematics and boundaries [36], classification of cardiac arrhythmias from electrocardiogram [37], and detection of cardiac structure and structural abnormalities [38]. However, direct application of a trained model to a previously unseen dataset often yields suboptimal performance [3,8,39].…”

Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification

“…Early arrhythmia classification methods focused on feature-based models or rule-based algorithms, and they have been changed to models with raw ECG data input or minimal modification [ 29 , 30 , 31 ]. Recently, arrhythmia-classification methods have focused on deep learning approaches, especially with big data [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. We omitted the feature-based methods and confined to deep learning approaches in accordance with the scope of this work.…”

“…These waves are the principal features of any ECG signal, which helps in the diagnosis of various arrhythmias [ 4 ]. Artificial intelligence (AI) techniques have recently been used in a variety of medical applications [ 5 , 6 , 7 , 8 ] and especially in the classification of arrhythmias [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. These AI techniques according to most of the previous works are classified into two main methods, such as feature-based approaches and deep learning approaches.…”

“…Deep learning obtains high accuracy by using big data and can overcome the overfitting problem [ 28 ]. Convolutional neural networks (CNNs), the most often employed deep learning method, have recently acquired prominence in the classification of arrhythmias by using ECG signals [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. Automatic detection of this disease plays an important role in the immediate diagnosis of the heart.…”

A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia

“…ECG-based arrhythmia classification can be performed using handcrafted and deep temporal features. In [ 8 ], the authors proposed a gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM)-based model for the classification of arrhythmia. They used six machine learning classifiers and achieved 90.42% accuracy for the 1D signal.…”